Semantic Image Retrieval Research Articles

An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques

An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques

Manifold-ranking embedded order preserving hashing for image semantic retrieval

Due to the storage and computational efficiency of hashing technology, it has proven a valuable tool for large scale similarity search. In many cases, the large scale data in real-world lie near some (unknown) low-dimensional and non-linear manifold. Moreover, Manifold Ranking approach can preserve the global topological structure of the data set more effectively than Euclidean Distance-based Ranking approach, which fails to preserve the semantic relevance degree. However, most existing hashing methods ignore the global topological structure of the data set. The key issue is how to incorporate the global topological structure of data set into learning effective hashing function. In this paper, we propose a novel unsupervised hashing approach, namely Manifold-Ranking Embedded Order Preserving Hashing (MREOPH). A manifold ranking loss is introduced to solve the issue of global topological structure preserving. An order preserving loss is introduced to ensure the consistency between manifold ranking and hamming ranking. A hypercubic quantization loss is introduced to learn discrete binary codes. The information theoretic regularization term is taken into consideration for preserving desirable properties of hash codes. Finally, we integrate them in a joint optimization framework for minimizing the information loss in each processing. Experimental results on three datasets for semantic search clearly demonstrate the effectiveness of the proposed method.

Multimodal Learning of Geometry-Preserving Binary Codes for Semantic Image Retrieval

Multimodal Learning of Geometry-Preserving Binary Codes for Semantic Image Retrieval

AN OBJECT PROPERTIES FILTER FOR MULTI-MODALITY ONTOLOGY SEMANTIC IMAGE RETRIEVAL

Ontology is a semantic technology that provides the possible approach to bridge the issue on semantic gap in image retrieval between low-level visual features and high-level human semantic. The semantic gap occurs when there is a discrepancy between the information that is extracted from visual data and the text description. In other words, there is a difference between the computational representation in machine and human natural language. In this paper, an ontology has been utilized to reduce the semantic gap by developing a multi-modality ontology image retrieval with the enhancement of a retrieval mechanism by using the object properties filter. To achieve this, a multi-modality ontology semantic image framework was proposed, comprising of four main components which were resource identification, information extraction, knowledge-based construction and retrieval mechanism. A new approach, namely object properties filter is proposed by customizing the semantic image retrieval algorithm and the graphical user interface to facilitate the user to engage with the machine i.e. computers, in order to enhance the retrieval performance. The experiment results showed that the proposed approach delivered better results compared to the approach that did not use the object properties filter based on probability precision measurement.

Ontology-Based Framework for Semantic Text and Image Retrieval Using Chord-length Shape Feature

Ontology-Based Framework for Semantic Text and Image Retrieval Using Chord-length Shape Feature

모바일 환경에서 의미 기반 이미지 어노테이션 및 검색

The progress of mobile computing technology is bringing a large amount of multimedia contents such as image. Thus, we need an image retrieval system which searches semantically relevant image. In this paper, we propose a semantic image annotation and retrieval in mobile environments. Previous mobile-based annotation approaches cannot fully express the semantics of image due to the limitation of current form (i.e., keyword tagging). Our approach allows mobile devices to annotate the image automatically using the context-aware information such as temporal and spatial data. In addition, since we annotate the image using RDF(Resource Description Framework) model, we are able to query SPARQL for semantic image retrieval. Our system implemented in android environment shows that it can more fully represent the semantics of image and retrieve the images semantically comparing with other image annotation systems.

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches.

Enhanced Semantic Image Retrieval using Feature Extraction and KNN Techniques

In addition of that, the technique required some additional techniques to correct the retrieval process such as user feedback, these methods consumes additional time of search. Thus a new technique with hybrid concept is proposed for improving the content based image search. The proposed technique includes the technique to train the system using the image features and text for annotation of image. For identifying the images more accurately the text and image features are used. Finally to retrieve the data (image) using user query (image or text) a KNN algorithm is implemented with it. The implementation of the proposed model is performed using visual studio technology and their performance in terms of time and space complexity is estimated. In addition of that the performance in terms of accuracy and error rate is also provided for demonstrating the relevancy of image search. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.

Incremental Hashing for Semantic Image Retrieval in Nonstationary Environments.

A very large volume of images is uploaded to the Internet daily. However, current hashing methods for image retrieval are designed for static databases only. They fail to consider the fact that the distribution of images can change when new images are added to the database over time. The changes in the distribution of images include both discovery of a new class and a distribution of images within a class owing to concept drift. Retraining of hash tables using all images in the database requires a large computation effort. This is also biased to old data owing to the huge volume of old images which leads to a poor retrieval performance over time. In this paper, we propose the incremental hashing (ICH) method to deal with the two aforementioned types of changes in the data distribution. The ICH uses a multihashing to retain knowledge coming from images arriving over time and a weight-based ranking to make the retrieval results adaptive to the new data environment. Experimental results show that the proposed method is effective in dealing with changes in the database.

State of the Art: A Summary of Semantic Image and Video Retrieval Techniques

Our paper comprises of almost maximum used state of the art techniques in video retrieval as a brief summary. As there is developments in all fields, media becomes more popular and so people begun to search videos to know the world happenings visually. This paper gives a review of existed combinational methods, algorithms and techniques. The image retrieval was obtained from multiple lower ranking methods. The survey of many methods leads to an introduction of combination of a best method. The main focus of this survey is focused on video retrieval techniques to identify the underlying factors that affect the performance of the combinational method and to judge the effectiveness to provide efficient semantic video retrieval. Finally we descript a broad discussion about the advantages and drawbacks that have been in state of the art. Due to these reasons semantic video retrieval became a challenging issue in various industries.

An Efficient Annotation based Image Retrieval System by Mining of Semantically Related user Queries with Improved Markovian Model

Objective: The main objective of this research is to establish the semantic gap between human-understandable high-level semantics and machine generated low-level features for Automatic image annotation in Annotation based Online Image Retrieval system. The semantic gap reduction is also concentrated where there will present more semantic gap between the human and machine defined entities. Methods/Statistical Analysis: Semantic annotated Markovian Semantic Indexing (SMSI) is used for retrieving the images and automatically annotates the images in the database using hidden Markov model. In contrast to traditional annotation based image retrieval system, retrieves images based on low-level features, the proposed SMSI semantically retrieves the images by searching semantically annotated images in a database for a user query. Each image in a large collection of training samples is then annotated automatically with the a posteriori probability of concepts present in it. At last semantic retrieval of images can be done by measuring semantic similarity of annotated images in the large database by using Natural Language processing tool namely WordNet. In addition to that entity based ontology representation is introduced which tend to map the human defined higher level keywords to the machine specific lower lever features. It is achieved by converting the lower level feature values into the intermediate level features. Findings: The presented SMSI method possess definite theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and Markovian Semantic Indexing (MSI), methods in Annotation-Based online Image Retrieval system. The better accuracy is achieved while retrieving the contents based image annotation where the semantic gap is reduced considerably. Application/Improvements: Thus the analysis of presented work is demonstrates semantically related features of images and achieves improved retrieval result when compare with the other state-of-art techniques.

Shape annotation for intelligent image retrieval

Annotation of shapes is an important process for semantic image retrieval. In this paper, we present a shape annotation framework that enables intelligent image retrieval by exploiting in a unified manner domain knowledge and perceptual description of shapes. A semi-supervised fuzzy clustering process is used to derive domain knowledge in terms of linguistic concepts referring to the semantic categories of shapes. For each category we derive a prototype that is a visual template for the category. A novel visual ontology is proposed to provide a description of prototypes and their salient parts. To describe parts of prototypes the visual ontology includes perceptual attributes that are defined by mimicking the analogy mechanism adopted by humans to describe the appearance of objects. The effectiveness of the developed framework as a facility for intelligent image retrieval is shown through results on a case study in the domain of fish shapes.

Sequential conditional entropy maximization semi-supervised hashing for semantic image retrieval

Hashing has been widely applied to large scale semantic image retrieval. Unsupervised hashing cannot work well for semantic image retrieval while supervised hashing requiring full label information for large databases is not practical. Semi-supervised hashing (SSH) solves this problem by learning the semantic information from a small portion of labeled images and the data structure information from the unlabeled images. The major drawback of the current SSH is that they cannot guarantee the maximization of entropy over all hash bits for a better coding efficiency. We propose a SSH which maximizes the conditional entropy of a bit with respect to all previous bits, i.e. the sequential conditional entropy maximization SSH. It is further extended to a nonlinear SSH with a new mapping method to enhance precision and recall rates. Experimental results show that the nonlinear SSH does not work well for all cases, and a heuristic guideline for the selection between linear and nonlinear hashing is given based on the characteristics of the database. We also propose a multiple hashing version of the proposed method for high recall rate with few hash bucket visits. Experimental results show that the proposed method outperforms current SSH methods.

Semantic Image Retrieval: An Ontology Based Approach

Images / Videos are major source of content on the internet and the content is increasing rapidly due to the advancement in this area. Image analysis and retrieval is one of the active research field and researchers from the last decade have proposed many efficient approaches for the same. Semantic technologies like ontology offers promising approach to image retrieval as it tries to map the low level image features to high level ontology concepts. In this paper, we have proposed Semantic Image Retrieval: An Ontology based Approach which uses domain specific ontology for image retrieval relevant to the user query. The user can give concept / keyword as text input or can input the image itself. Semantic Image Retrieval is based on hybrid approach and uses shape, color and texture based approaches for classification purpose. Mammals domain is used as a test case and its ontology is developed. The proposed system is trained on Mammals dataset and tested on large number of test cases related to this domain. Experimental results show the efficiency / accuracy of the proposed system and support the implementation of the same.

Semantic Image Analysis for Intelligent Image Retrieval

Abstract Image understanding and analysis is the most exciting and fastest-growing research areas in the computer vision. Recent computer vision technologies and algorithms are support efficient semantic image analysis and retrieval. Image analysis is deal with image representation, estimation formula, and sampling density. Image analysis at semantic level is result in automatic extraction of image descriptions as per human perception which ultimately bridge semantic gap between low-level visual features and the high-level concepts capturing the conveyed meaning. Vital semantic image information is basically retrieved from image content, mainly from meaningful image objects and their mutual relations. In this paper, we present Semantic analysis of image by knowledge driven approach, start with Image content analysis with respect to semantic concepts, design image database and knowledge base on the basis of semantic content and retrieval, presentation and modification of image reference database or knowledge base for knowledge delivery intention. Experimental result shows improvement in image retrieval performance and accuracy.

Self-Growing RBF Neural Network Approach for Semantic Image Retrieval

Traditional methods of content-based image retrieval deal with the retrieval of images according to the similari- ty between them and the sample image in some low-level feature space such as color, shape and structure. But the relevant images satisfying user information need tend to have different distribution in the low-level feature space. In this case, the query image needs to be represented as multiple query images corresponding to the scattered relevant images. This paper proposes a new relevance feedback technique for semantic image retrieval which is based on the self-growing radial basis function (SGRBF) neural network. The approach can adaptively construct SGRBF neural network based on the users' feedbacks. Thus, hidden nodes of the SGRBF neural network can represent the distribution of the users' perceptual in the low-level feature space and bridge the semantic gap between low-level feature and high-level concept of the image con- tent. The method is verified on a database of 1000 images and experimental results demonstrate that our method proposed in this paper is an effective method to promote semantic image retrieval performance.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic "soft" prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies.

Semisupervised Hashing via Kernel Hyperplane Learning for Scalable Image Search

Hashing methods that aim to seek a compact binary code for each image are demonstrated to be efficient for scalable content-based image retrieval. In this paper, we propose a new hashing method called semisupervised kernel hyperplane learning (SKHL) for semantic image retrieval by modeling each hashing function as a nonlinear kernel hyperplane constructed from an unlabeled dataset. Moreover, a Fisher-like criterion is proposed to learn the optimal kernel hyperplanes and hashing functions, using only weakly labeled training samples with side information. To further integrate different types of features, we also incorporate multiple kernel learning (MKL) into the proposed SKHL (called SKHL-MKL), leading to better hashing functions. Comprehensive experiments on CIFAR-100 and NUS-WIDE datasets demonstrate the effectiveness of our SKHL and SKHL-MKL.

Semantically Effective Visual Concept Illustration for Images

The field of analyzing and understanding high-dimension real world data in a knowledgeable form is known as computer vision. Image is such a high dimension real world data which is enormously available in meaningless way. Using Ontologies in this computer vision field will enable a high end Ontology knowledge based image analysis and understanding. The main objective of this paper is to develop a frame work that creates a Bag of Visual Words for sports events using low level features of images and to develop ontology to provide semantic meaning to the images. Such semantically available images can be easily retrieved using a semantic image retrieval system. We used the concepts of enhanced Scale Invariant Feature Transform (SIFT) for feature extraction and Support Vector Machine based classification model to provide semantic to the images for action classification of images

An Express Retrieval Method of Face Based on Semantic Features

The paper uses digital image processing technology, technology of face pattern recognition and traditional database retrieval technology, integrate image retrieval technology based on version and content, and avoid the complexity of matching image process. The experiments with 200 human samples, correctly retrieved for 155 people, exactly matched to 125. The recognition rate of the system is 75.55%, the average time of search is less than 0.1s. Experiments indicate this method has strong robustness. The semantic face image retrieval system using this method has the characteristics of fast, efficient, practical.